Trade Bitcoin and run forecasting models from the terminal

Using multidimensional LSTM neural networks to create a forecast for Bitcoin price

GARCH and Multivariate LSTM forecasting models for Bitcoin realized volatility with potential applications in crypto options trading, hedging, portfolio management, and risk management

Time Series Forecast with Bitcoin value, to detect upward/down trends with Machine Learning Algorithms

No description available

This repository contains the code and datasets for creating the machine learning models in the research paper titled "Time-series forecasting of Bitcoin prices using high-dimensional features: a machine learning approach"

Forecasting Bitcoin Volatility Spikes from Whale Transactions and Cryptoquant Data Using Synthesizer Transformer Models

Forecasting Bitcoin Prices via ARIMA, XGBoost, Prophet, and LSTM models in Python

Time Series analysis with Python and ARIMA model to forecast Bitcoin price

Time Series Forecasting of Bitcoin Prices using LSTM and RNN with Particle Swarm Optimization and Grey Wolf Optimizer

Using Facebook's Prophet forecasting library to forecast bitcoin prices

:mortar_board: Social media text data scraping for sentiment analysis (Vader, Bert), correlation study, and Bitcoin price prediction using LSTM and XGBoost, with a comparative performance analysis across various media sources like forums and group chats.

This repository is part of an article about how to forecast and detect anomalies on time-series data. The main objective is to train a RNN regressor on the Bitcoin dataset to predict future values on then detect anomalies in the whole data window - that last step achieved by implementing a RNN Autoencoder. You'll see some other models in the notebooks that I've provided to you in case they are of your interest and this RNN regressor + RNN Autoencoder doesn't perform well for your purpose in any other scenario. The dataset used is available at https://www.kaggle.com/mczielinski/bitcoin-historical-data and contains BITCOIN/USD 1-minute candle data, from 2012-01-01 to 2020-12-31. I hope you can get advantage of this approach!

Bitcoin Forecasting using VAR, XGBoost, and Facebook Prophet

Applied various machine learning algorithms like KNN regressor, Support Vector Machine regresson, Linear, Polynomial, Ridge and Lasso regressions. Identified best model using Grid Search. Calculated accuracy of the models using cross validation and predicted bitcoin prices using the best model.

ThermoHash 🌡️ – AI-powered Bitcoin miner control & heat-reuse automation. From home heating with a single S19 to managing industrial mining farms, ThermoHash uses weather forecasts, Bitcoin prices, and machine learning to automatically tune power and maximize profit—while turning waste heat into useful warmth.

Bitcoin Price Prediction model - LSTM | Multivariable (Price&Polarity) Time Series Forecasting with NLP for Twitter Sentiments aka my Master's Thesis

No description available

Forecasting Bitcoin returns through time series analysis, emphasizing sentiment analysis on news (using BERT LLM), social media, and Google search trends, with the final model based on Random Forest, augmented with engineered memory features.

Timeseries forecast on Bitcoin stock price with ARIMA, Prophet, LSTM Recurrent Neural Network and XGBoost.

For this project, I used Bitcoin's daily closing market price dataset from Jan 2012 to March 2021 Kaggle. This work's main objective includes explaining how to analyze a time series and forecast its values using ARIMA and GARCH models.

Python jupyter notebook to forecast bitcoin closing prices

LSTM-based Bitcoin price prediction using PyTorch. Explore deep learning for time-series forecasting with data preprocessing and model training. Ideal for cryptocurrency enthusiasts and those interested in financial forecasting.

Bitcoin's Value Forecast using ARIMA model, in contrast to an Exponential Smoothing approach.

This project implements a Transformer-based time series prediction model to forecast 6-hour future returns of Bitcoin (BTC/USDT) using Binance OHLCV data

Mine webpages for numerical data and forecast bitcoin popularity.

Stock and Bitcoin time-series forecasting using machine learning

This repository contains implementations of Long Short-Term Memory (LSTM) models for predicting the closing prices of three major cryptocurrencies: Bitcoin (BTC), Solana (SOL), and XRP. The project leverages historical OHLC data to train deep learning models capable of forecasting future price trends.

A predictive model for forecasting the hourly Bitcoin price movements using sentiment analysis drawn from extracted from Twitter data and tree based algorithms. The resulting predictive model utilises an XGBoost gradient boosted random forest model which demonstrates a 63.16% accuracy in predicting the hourly price movements of Bitcoin.

Forecasting the price of Bitcoin’s during or post 2024 halving using Facebook Prophet